Critical Value Calculator

Find critical values for z, t, and chi-square distributions at any significance level and tail type.

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Distribution

Tail type

Critical Value

±1.960395

The two-tailed critical value for the standard normal (z) distribution at α = 0.05 is ±1.960395.

Significance (\u03B1)
0.05
Distribution
standard normal (z)
Lower critical value
-1.960395
Upper critical value
1.960395

Rejection region

z < -1.960395 or z > 1.960395

Also in Statistics

Hypothesis Testing

Critical value calculator: find z, t, and chi-square critical values

A critical value calculator determines the threshold test statistic for rejecting the null hypothesis at a given significance level. It supports z (standard normal), t (Student), and chi-square distributions with left-tailed, right-tailed, and two-tailed options.

How critical values work

A critical value marks the boundary of the rejection region in a hypothesis test. If the test statistic falls beyond the critical value, the null hypothesis is rejected. For a two-tailed test at α = 0.05, the z critical values are ±1.96, meaning the most extreme 5% of the distribution is split between both tails.

For t-tests and chi-square tests, critical values depend on the degrees of freedom. As degrees of freedom increase, t critical values approach z critical values. Chi-square critical values are always right-tailed because the chi-square distribution is non-negative.

zₐ = Φ⁻¹(1 − α)

Right-tailed z critical value from the inverse standard normal CDF.

z_{α/2} = Φ⁻¹(1 − α/2)

Two-tailed z critical value.

Frequently asked questions

Why does the chi-square test only use right-tailed critical values?

The chi-square distribution is defined for non-negative values only. Test statistics measure squared deviations, so only large values provide evidence against the null hypothesis, making the test inherently right-tailed.

How do I choose between z and t critical values?

Use z when the population standard deviation is known or the sample size is large (n > 30 is a common rule of thumb). Use t when working with small samples and an estimated standard deviation, specifying degrees of freedom as n - 1.

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